This invention relates in general to complex signal transformations, and more particularly, to a resistive network for performing a simultaneous complex transformation of the spatial vector formed by sampling an input signal.
Complex signal transformations such as the Fourier, Laplace and convolution integrals are routinely encountered in modern signal processing. The Fourier transform, for example, is used to convert data from one dimension, typically time domain, into another dimension, frequency domain. This conversion is advantageous in that once the data is converted to the appropriate dimension, the individual components of that dimension may be easily manipulated to achieve the desired result. The fast Fourier transform (FFT) and the discrete Fourier transform (DFT) are conventional estimations of the Fourier integral typically calculated through sequential iterations of the input data. Likewise, the inverse discrete Fourier transform (IDFT), which transposes the data back to the original dimension, is also computed in a serial fashion.
The serial transformation is still desirable in some signal processing applications wherein a precise quantitative output is required. However, many input signals are real time in nature and the conventional sequential numeric processing of the DFT, or for that matter, any other complex transformation, creates a bottleneck in the signal path limiting the bandwidth of the input signal. Applications such as speech and pattern recognition and video image processing are much more concerned with data throughput than numeric precision, and as such, are ideal candidates for the inherent parallel processing techniques of neural networks.
Thus, what is needed is a parallel processing technique to improve the data throughput of a complex transform.